8 October 2007 Video scene assessment with unattended sensors
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Abstract
This paper describes an automated video analysis based monitoring system with processing at the sensor edge to watch and report certain predetermined events and unusual activities in remote areas that may be in unfriendly zones. The prototype system developed here involves content extraction from video streams collected by unattended ground cameras, tracking of objects, detection of events, and assessment of scenes for anomalous situations. The application requirements impose efficiency constraints on video analysis algorithms due to low-power on the sensor processing board. We present efficient video analysis algorithms for detection, tracking and classification of objects, analysis of extracted object and scene information to detect specific events as well as anomalous or novel situations at the video camera level. Our multi-tier and modular video analysis approach uses fast a space-based peripheral vision component for quick spatially based tracking of objects, detailed object- or scene-based feature extractors and data driven Support Vector Machine (SVM) classifiers that handle feature-based analysis at multiple data levels. Our algorithms are developed and tested on PC platform but designed to match the processing and power limitations of the target hardware platform. The video object detection and tracking components have been implemented on Texas Instruments DM642 evaluation board for assessing the feasibility of the prototype system.
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Sadiye Guler, Kshitiz Garg, and Jason A. Silverstein "Video scene assessment with unattended sensors", Proc. SPIE 6741, Optics and Photonics for Counterterrorism and Crime Fighting III, 674108 (8 October 2007); doi: 10.1117/12.738843; https://doi.org/10.1117/12.738843
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